Burned area detection and estimation of fire carbon emissions in Canada in 2023 using Landsat 8/9 and Sentinel 2 data
Bibliographic record
Abstract
The extreme wildfires in Canada of 2023 have drawn significant attention. Satellite remote sensing provides an effective way for monitoring burned area (BA). However, the existing BA products for Canada in 2023 are mainly at medium and low spatial resolution. In order to precisely detect BA and fire carbon emissions, Landsat 8/9 and Sentinel-2 data were utilized to extract higher resolution (30 m/20 m) BA in Canada of 2023. Based on the simplified GFED (Global Fire Emissions Database) model, a fire-induced carbon emission model for Canada was constructed. 30 m/20 m resolution BA were used as model input to estimate fire carbon emissions in Canada in 2023. The results show that the BA in Canada of 2023 reached 103,000 km2, which was 4.7 times the average BA from 1986 to 2022. Compared to 1986–2022, the proportion of large wildfires in Canada of 2023 increased significantly. The CO2 emissions from the 2023 wildfires in Canada exceeded 1.400 billion tons, surpassing the total CO2 emissions from fossil fuel combustion in Japan and France in 2022. We analysed trends of drought index and its correlations with BA, and found that the abnormal drought in Canada in 2023 may be one of the reasons for the extreme wildfires.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".